Deciphering the complex interplay between microbiota, HPV, inflammation and cancer through cervicovaginal metabolic profiling

Abstract

Background: Dysbiotic vaginal microbiota have been implicated as contributors to persistent HPV-mediated cervical carcinogenesis and genital inflammation with mechanisms unknown. Given that cancer is a metabolic disease, metabolic profiling of the cervicovaginal microenvironment has the potential to reveal the functional interplay between the host and microbes in HPV persistence and progression to cancer.

Methods: Our study design included HPV-negative/positive controls, women with low-grade and high-grade cervical dysplasia, or cervical cancer (n = 78). Metabolic fingerprints were profiled using liquid chromatography-mass spectrometry. Vaginal microbiota and genital inflammation were analysed using 16S rRNA gene sequencing and immunoassays, respectively. We used an integrative bioinformatic pipeline to reveal host and microbe contributions to the metabolome and to comprehensively assess the link between HPV, microbiota, inflammation and cervical disease.

Findings: Metabolic analysis yielded 475 metabolites with known identities. Unique metabolic fingerprints discriminated patient groups from healthy controls. Three-hydroxybutyrate, eicosenoate, and oleate/vaccenate discriminated (with excellent capacity) between cancer patients versus the healthy participants. Sphingolipids, plasmalogens, and linoleate positively correlated with genital inflammation. Non-Lactobacillus dominant communities, particularly in high-grade dysplasia, perturbed amino acid and nucleotide metabolisms. Adenosine and cytosine correlated positively with Lactobacillus abundance and negatively with genital inflammation. Glycochenodeoxycholate and carnitine metabolisms connected non-Lactobacillus dominance to genital inflammation.

Interpretation: Cervicovaginal metabolic profiles were driven by cancer followed by genital inflammation, HPV infection, and vaginal microbiota. This study provides evidence for metabolite-driven complex host-microbe interactions as hallmarks of cervical cancer with future translational potential. FUND: Flinn Foundation (#1974), Banner Foundation Obstetrics/Gynecology, and NIH NCI (P30-CA023074).

Keywords: Amino acid degradation; Cervical dysplasia and cancer; Genital inflammation; Host-microbe interactions; Lactobacillus abundance; Lipids and nucleotides; Vaginal dysbiosis.

Graphical abstract

Fig. 1

HPV infection, dysplasia, and invasive cervical carcinoma (ICC) divergently impacted the cervicovaginal metabolic profile. A) Principal component analysis (PCA) showed a distinct cervicovaginal metabolome profile in ICC patients compared to Ctrl HPV (−), Ctrl HPV (+), LSIL, and HSIL patient groups. The difference was significant on both PC1 and PC2 axes. HSIL group was significantly different than Ctrl HPV (−) group on PC2 axis. Boxplots represent median, first and third quartile, minimum and maximum values in the dataset. Shaded ellipses represent 95% confidence intervals of the cluster centroids. Mann-Whitney U test p values represented as *p  < .05, **p < .01, ***p < .001. B) Number of metabolites detected in each group. ICC patients had significantly greater number of metabolites in comparison to patients in the Ctrl HPV (+), LSIL, and HSIL groups. However, there was no significant difference between the ICC and Ctrl HPV (−) groups. ns = not significant. The trend analysis showed that the trend was polynomial (p = .01). C) Number of unique and shared metabolites among the groups visualized on a Venn diagram. Majority of the metabolites were detected in the all groups. ICC group had the greatest number of metabolites that were not detected in any of the participants in other groups. Supplementary Table 2 contains the list of metabolites that are different in each group. D) Enrichment and depletions of metabolites among patient groups visualized by Cytoscape metabolic network analysis. The node size is proportional to the magnitude of differences observed among the groups. Red and blue nodes represent enriched and depleted metabolites, respectively. In comparison to all other patient groups, ICC patients had an enrichment of metabolites that belong to lipid, amino acid, carbohydrate, and xenobiotic metabolism. Amino acids and their metabolites were depleted in Ctrl HPV (+), LSIL, and HSIL groups. Dipeptides were significantly depleted in the HSIL group compared to Ctrl HPV (−). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Multiple metabolites from diverse pathways robustly discriminated healthy, dysplasia, and ICC patient groups. The cut-off value for the Receiver Operating Characteristics (ROC) analysis was 0.8 to include only good (0.8 ≤ AUC < 0.9) and excellent (AUC ≥ 0.9) biosignatures. A) ROC analysis comparing Ctrl HPV (−) to Ctrl HPV (+). ROC showed that amino acid, lipid, nucleotide, and xenobiotic metabolism products were indicators of absence of HPV infection. B) ROC comparing Ctrl HPV (−) to LSIL. Depletions in the amino acid metabolism products in LSIL discriminated Ctrl HPV (−) from LSIL. C) ROC comparing Ctrl HPV (−) to HSIL. Phosphoethanolamine and glutamine were discriminators of Ctrl HPV (−) from HSIL. D) ROC analysis performed between ICC and Ctrl HPV (−) groups revealed metabolites from different lipid classes with >0.9 area under the curve (AUC) values serve as strong discriminators of ICC. Those lipids were associated with cancer related inflammation. Supplementary Table 3 contains the median levels of the metabolites and Supplementary Table 4 contains the q values of Welch's t-test for group comparisons listed in this figure.

Fig. 3

Vaginal microbiota composition profoundly impacted amino acid and nucleotide metabolisms. A) Principal component analysis of metabolomes visualized based on Lactobacillus dominance. Lactobacillus dominant (LD) and non-Lactobacillus dominant (NLD) metabolomes were significantly different on PC2. Boxplots represent median, first and third quartile, minimum and maximum values in the dataset. Shaded ellipses represent 95% confidence intervals of the cluster centroids. Mann-Whitney U test p values represented as *p < .05, **p < .01, ***p < .001. B) Metabolic enrichments and depletions in NLD vs LD visualized on Cytoscape networks. Metabolomes of patients that had NLD vaginal microbial communities had enrichments in lipid and amino acid metabolism. Dipeptides were depleted in patients with NLD communities. Some metabolites from polyamines were enriched whereas some were depleted in patients with NLD communities. C) Prediction of vaginal microbiota or host origins of the metabolites. PICRUSt predicted metagenomes annotated with AMON explained 60% of the detected metabolites in cervicovaginal lavages in all groups. D) The six most abundant genera within the data set were predicted to contribute 14–23% of the metabolites that were detected. The microbially produced metabolites mainly belonged to amino acid and nucleotide metabolism. E) Well-predicted (FDR < 0.1) metabolites based on MIMOSA analysis and relative contribution of microbial phylotypes to the production or depletion of metabolites. The six most dominant genera within our dataset explained the observed concentrations of many amino acids and their metabolic end-products.

Fig. 4

Levels of genital inflammation highly correlated with metabolite profiles and patient groups. A) Principal component analysis (PCA) demonstrated a gradual separation of metabolomes based on presence or intensity of genital inflammation. Metabolomes of patient with high genital inflammation (inflammation score of 5–7) formed a separate cluster from patients without (inflammation score = 0) or low genital inflammation (inflammation score of 1–4) on PC1 and PC2. Majority of the high genital inflammation samples belonged to ICC patients. Boxplots represent median, first and third quartile, minimum and maximum values in the dataset. Shaded ellipses represent 95% confidence intervals of the cluster centroids. Mann-Whitney U test p values represented as *p < .05, **p < .01, ***p < .001. B) Number of metabolites positively and negatively correlated with genital inflammation. Spearman's rho correlation coefficient greater than the critical value (0.23 for n = 78) with a bootstrapped p < .05 were considered significant. Metabolites from lipid metabolism positively correlated with genital inflammation whereas xenobiotics negatively correlated with the genital inflammation. Lipids that correlated with genital inflammation belonged to sphingomyelins, plasmalogens, phosphatidylcholines (PC), long-chain-polyunsaturated fatty acids, and phosp hatidylethanolamines (PE). Supplementary Table 5 includes the list of Spearman's correlation coefficients reflecting the strength of correlation between genital inflammation and metabolites. C) Relative levels of plasmalogens, sphingomyelins, phosphatidylcholines, phosphatidylethanolamines, and long-chain polyunsaturated fatty acid metabolites were greater in the ICC group compared to the others. Boxplots represent median, first and third quartile, minimum and maximum values in the dataset. Welch's t-test p values represented as *p < .05, **p < .01, ***p < .001.

Fig. 5

A) Metabolites that connected genital inflammation to Lactobacillus abundance. Scatter plot of metabolites that correlated with genital inflammation and Lactobacillus abundance. Lipids (plasmalogens, sphingomyelin, phosphatidylcholine, and long chain polyunsaturated fatty acids (LCPUFA)) strongly and positively correlated with genital inflammation but not with Lactobacillus abundance. A few amino acids negatively correlated with Lactobacillus abundance positively correlated with genital inflammation. Carnitine metabolites positively correlated with both Lactobacillus abundance and genital inflammation. Biogenic amines negatively correlated with Lactobacillus abundance but did not correlate with genital inflammation. B) Relative levels of metabolites that positively correlated with genital inflammation and negatively correlated with Lactobacillus abundance among the patient groups. The concentrations were relatively higher in the ICC group. C) Relative levels of metabolites that positively correlated with genital inflammation and Lactobacillus abundance among the patient groups. The concentrations were relatively higher in the ICC group. D) Relative levels of metabolites that negatively correlated with genital inflammation and positively with Lactobacillus abundance among the patient groups. The concentrations were relatively lower in the ICC group. Metabolites with the three highest ρ values were visualized on graphs B, C, and D. E) Relative levels of metabolites that had significant associations with Lactobacillus-dominance or dysbiotic microbiota. Welch's t-test p values represented as *p < .05, **p < .01, ***p < .001.

Fig. 6

Metabolome profiles were driven by the features of the cervicovaginal microenvironment – HPV infection, cancer, non-Lactobacillus dominated microbiota, and genital inflammation. A) Hierarchical clustering (HCA) dendrogram constructed using top-down approach presented with patient characteristics including HPV status, genital inflammation score, vaginal pH, and microbial community based on Lactobacillus abundance. HCA analysis revealed three main clusters. B) Distribution of patient groups in the clusters. Cluster 1 only contained ICC patients whereas Cluster 2 and 3 contained patients from all the groups. The patient group composition of Cluster 1 was significantly different than Cluster 2 based on chi-square test. C) Genital inflammation scores among the clusters. Genital inflammation score of Cluster 1 was significantly higher than the scores of Clusters 2 and 3. Boxplot plots demonstrate median, 25th and 75th quartiles. D) Percentage of samples belonging to patients with Lactobacillus dominant and non-Lactobacillus dominant microbiota. Cluster 1 and 3 were dominated by non-Lactobacillus dominated communities whereas Cluster 2 had greater number of samples that were Lactobacillus dominated. Based on chi-square test, Cluster 2 was significantly different than Cluster 1 and Cluster 3. E) Vaginal pH measurements among the clusters. Cluster 1 and 3 had samples from patients with higher vaginal pH compared to Cluster 2 patients. * p < .5, **p < .01, ***p < .001, ****p < .0001.